This paper presents a new technique which exploits constrained optimization methods to derive optimal two dimensional filters in the cumulant domain for processing signals in non Gaussian noise, or signals with corrupting interferences which have non symmetrical probability density functions. The approach proposed here for enhancing signals in such noise is important, as increasingly practical ...

In this paper, I compare model-based and moving average-based approaches to seasonal adjustment. I propose an optimal moving average filter, designed to minimize the distance from a model-based filter. In simulations, I consider the accuracy of the alternative methods in estimating seasonal factors in a simple model. I find that the most reliable results are obtained either by the model-based a...

Graph signal processing has recently received considerable attention. Several concepts, tools, and applications in such as filtering, transforming, sampling have been extended to graph processing. One extension is the optimal filtering problem. The minimum mean-squared error estimate of an original can be obtained from its distorted noisy version. However, best separation noise, thus least erro...